We trained a ResNet-18 neural network (pretrained on ImageNet) [1]} on all images from all superclasses of the Superclassing ImageNet dataset to make a 10-way superclass prediction. We named it the  Superclass Network. The high values on the diagonal of the confusion matrix in Fig. REF  mean that the Superclass Network barely makes any inter-superclass mistakes, as the averaged correct prediction performance is 96.52%. For example, the probability of recognizing a bird image as belonging to the Bird superclass is 96.77%, while the probability of recognizing a bird image as belonging to the Car superclass is only 0.08%. In addition, for a network that is still trained on all images from all superclasses, but for which the goal is to perform a 253-way classification directly among all subclasses, the averaged correct prediction performance decreases to 71.18%. This lower performance suggests that most misclassifications are in intra-superclass predictions.
